Search Results for author:
Found 23 papers, 9 papers with code
Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
Accordingly, we propose Vary, an efficient and effective method to scale up the vision vocabulary of LVLMs.
Ranked #50 on
Visual Question Answering
on MM-Vet
Optical Character Recognition (OCR)
Visual Question Answering
Merlin:Empowering Multimodal LLMs with Foresight Minds
Then, FIT requires MLLMs to first predict trajectories of related objects and then reason about potential future events based on them.
Ranked #59 on
Visual Question Answering
on MM-Vet
DreamLLM: Synergistic Multimodal Comprehension and Creation
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation.
Ranked #1 on
Visual Question Answering
on MMBench
(GPT-3.5 score metric)
GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping
Besides, GroupLane with ResNet18 still surpasses PersFormer by 4. 9% F1 score, while the inference speed is nearly 7x faster and the FLOPs is only 13. 3% of it.
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Based on precise referring instruction, we propose ChatSpot, a unified end-to-end multimodal large language model that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience.
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection
In light of the success of sample mining techniques in 2D object detection, we propose a simple yet effective mining strategy for improving depth perception in 3D object detection.
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo
Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck.
Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception
In this paper, we explore an embarrassingly simple long-term recurrent fusion strategy built upon the LSS-based methods and find it already able to enjoy the merits from both sides, i. e., rich long-term information and efficient fusion pipeline.
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation
VLM joints the information in the generated visual prompts and the textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description, which is domain invariant to different tracking scenes.
Towards 3D Object Detection with 2D Supervision
We propose a temporal 2D transformation to bridge the 3D predictions with temporal 2D labels.
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo
To this end, we introduce an effective temporal stereo method to dynamically select the scale of matching candidates, enable to significantly reduce computation overhead.
Ranked #11 on
3D Object Detection
on nuScenes Camera Only
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking
The point cloud based 3D single object tracking has drawn increasing attention.
Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking
Recent advanced works generally employ a series of object attributes, e. g., position, size, velocity, and appearance, to provide the clues for the association in 3D MOT.
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference.
StreamYOLO: Real-time Object Detection for Streaming Perception
In this paper, we explore the performance of real time models on this metric and endow the models with the capacity of predicting the future, significantly improving the results for streaming perception.
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View (BEV) 3D object detection.
Ranked #4 on
3D Object Detection
on Rope3D
A Semantic Consistency Feature Alignment Object Detection Model Based on Mixed-Class Distribution Metrics
Then, a Semantic Consistency Feature Alignment Model (SCFAM) based on mixed-classes $H-divergence$ was also presented.
Real-time Object Detection for Streaming Perception
In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem.
Ranked #1 on
Real-Time Object Detection
on Argoverse-HD (Full-Stack, Val)
(sAP metric, using extra
training data)
Rectifying the Shortcut Learning of Background for Few-Shot Learning
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL).
Gaussian Guided IoU: A Better Metric for Balanced Learning on Object Detection
This results in two problems: (1) only one anchor is assigned to most of the slender objects which leads to insufficient supervision information for the slender objects during training and the performance on the slender objects is hurt; (2) IoU can not accurately represent the alignment degree between the receptive field of the feature at the anchor's center and the object.
Carton dataset synthesis method for domain shift based on foreground texture decoupling and replacement
To solve this problem, a novel image synthesis method is proposed to replace the foreground texture of the source datasets with the texture of the target datasets.
SCD: A Stacked Carton Dataset for Detection and Segmentation
In this paper, we present a large-scale carton dataset named Stacked Carton Dataset(SCD) with the goal of advancing the state-of-the-art in carton detection.
IoU-balanced Loss Functions for Single-stage Object Detection
The IoU-balanced localization loss decreases the gradient of examples with low IoU and increases the gradient of examples with high IoU, which can improve the localization accuracy of models.
